- #
 - #  Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
 - #
 - #  Licensed under the Apache License, Version 2.0 (the "License");
 - #  you may not use this file except in compliance with the License.
 - #  You may obtain a copy of the License at
 - #
 - #      http://www.apache.org/licenses/LICENSE-2.0
 - #
 - #  Unless required by applicable law or agreed to in writing, software
 - #  distributed under the License is distributed on an "AS IS" BASIS,
 - #  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 - #  See the License for the specific language governing permissions and
 - #  limitations under the License.
 - #
 - from abc import ABC
 - from api.db import LLMType
 - from api.db.services.llm_service import LLMBundle
 - from agent.component import GenerateParam, Generate
 - from api.utils.log_utils import logger
 - 
 - 
 - class CategorizeParam(GenerateParam):
 - 
 -     """
 -     Define the Categorize component parameters.
 -     """
 -     def __init__(self):
 -         super().__init__()
 -         self.category_description = {}
 -         self.prompt = ""
 - 
 -     def check(self):
 -         super().check()
 -         self.check_empty(self.category_description, "[Categorize] Category examples")
 -         for k, v in self.category_description.items():
 -             if not k: raise ValueError("[Categorize] Category name can not be empty!")
 -             if not v.get("to"): raise ValueError(f"[Categorize] 'To' of category {k} can not be empty!")
 - 
 -     def get_prompt(self):
 -         cate_lines = []
 -         for c, desc in self.category_description.items():
 -             for l in desc.get("examples", "").split("\n"):
 -                 if not l: continue
 -                 cate_lines.append("Question: {}\tCategory: {}".format(l, c))
 -         descriptions = []
 -         for c, desc in self.category_description.items():
 -             if desc.get("description"):
 -                 descriptions.append(
 -                     "--------------------\nCategory: {}\nDescription: {}\n".format(c, desc["description"]))
 - 
 -         self.prompt = """
 -         You're a text classifier. You need to categorize the user’s questions into {} categories, 
 -         namely: {}
 -         Here's description of each category:
 -         {}
 - 
 -         You could learn from the following examples:
 -         {}
 -         You could learn from the above examples.
 -         Just mention the category names, no need for any additional words.
 -         """.format(
 -             len(self.category_description.keys()),
 -             "/".join(list(self.category_description.keys())),
 -             "\n".join(descriptions),
 -             "- ".join(cate_lines)
 -         )
 -         return self.prompt
 - 
 - 
 - class Categorize(Generate, ABC):
 -     component_name = "Categorize"
 - 
 -     def _run(self, history, **kwargs):
 -         input = self.get_input()
 -         input = "Question: " + (list(input["content"])[-1] if "content" in input else "") + "\tCategory: "
 -         chat_mdl = LLMBundle(self._canvas.get_tenant_id(), LLMType.CHAT, self._param.llm_id)
 -         ans = chat_mdl.chat(self._param.get_prompt(), [{"role": "user", "content": input}],
 -                             self._param.gen_conf())
 -         logger.debug(f"input: {input}, answer: {str(ans)}")
 -         for c in self._param.category_description.keys():
 -             if ans.lower().find(c.lower()) >= 0:
 -                 return Categorize.be_output(self._param.category_description[c]["to"])
 - 
 -         return Categorize.be_output(list(self._param.category_description.items())[-1][1]["to"])
 - 
 - 
 
 
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